Social interaction targets enhance 13-month-old infants' associative learning

Infants are attentive to third-party interactions, but the underlying mechanisms of this preference remain understudied. This study examined whether 13-month-old infants (N = 32) selectively learn cue–target associations guiding them to videos depicting a social interaction scene. In a visual learning task, two geometrical shapes were repeatedly paired with two kinds of target videos: two adults interacting with one another (social interaction) or the same adults acting individually (non-interactive control). Infants performed faster saccadic latencies and more predictive gaze shifts toward the cued target region during social interaction trials. These findings suggest that social interaction targets can serve as primary reinforcers in an associative learning task, supporting the view that infants find it intrinsically valuable to observe others’ interactions.     

AN ANALYSIS OF SHAREHOLDER AGREEMENTS

Shareholder agreements govern the relations among shareholders in privately held firms, such as joint ventures and venture capital‐backed companies. We provide an economic explanation for key clauses in such agreements—namely, put and call options, tag‐along and drag‐along rights, demand and piggy‐back rights, and catch‐up clauses. In a dynamic moral hazard setting, we show that these clauses can ensure that the contract parties make efficient ex ante investments in the firm. They do so by constraining renegotiation. In the absence of the clauses, ex ante investment would be distorted by unconstrained renegotiation aimed at (i) precluding value‐destroying ex post transfers, (ii) inducing value‐increasing ex post investments, or (iii) precluding hold‐out on value‐increasing sales to a trade buyer or the IPO market. (JEL: G34)     

No place like home? Availability,opportunity, and substance use in adolescence

Surveys on drugs and alcohol availability show that (1) alcohol is much more easily accessible than both cannabis and tobacco, and that (2) many adolescents still do not find it ‘easy’ to access those substances. In the current study, we examine the importance of both opportunity and availability in assessing substance use in adolescence. Our measures of opportunity include skipping school and unstructured socializing, while availability measures include parental substance use and perceived availability of tobacco, alcohol, and cannabis at home. We draw from two waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health) survey, and distinguish between (a) predictors of Wave II substance use for the whole sample, and (b) predictors of Wave II initiation for those respondents who reported no use at Wave I. We find that availability at home and parental substance use is correlated with substance-specific current adolescent consumption, but these indicators are not associated with initiation behavior. Instead, initiation seems to be carried by unstructured socializing outside of the home, with availability at home acting as reinforcement once some level of substance use is established.     

Diversity of romantic experiences in late adolescence and their contribution to identity formation

Romance constitutes a central experience in adolescence, which can take several forms including (a) serious romance with a steady partner, (b) casual and short‐lived dating, and (c) pending experiences of romantic thoughts and attempts. Using a three‐way longitudinal design, the present study examined the gender differences and developmental changes of these romantic experiences, and their cross‐lagged associations with identity processes, throughout one year in late adolescence. 389 students (63% girls) filled out questionnaires about their romantic experiences and identity processes. Girls reported more serious romance than did boys, who reported more casual dating and pending experiences. Serious romance increased over the year for both genders whereas casual dating and pending experiences continued to rise only for boys. The cross‐lagged model highlighted that serious romantic experience positively predicted identity commitments and exploration in depth whereas identity exploration in breadth positively predicted casual dating. This pattern was stable over the three time points and across gender and age groups. Overall, this study invites consideration of more diverse romantic experiences than those associated with tangible involvement. We discuss the contribution of romantic experiences to the identity dynamic.     

作为精神分析的聆听——关于中国孤独症儿童家长的精神分析的讲座

2002年12月2日,现任欧洲和中国精神分析互助联合会主席、法国精神分析家米歇尔·吉布尔(MichelGuibal)先生应邀在北京大学就有关中国孤独症儿童家长的精神分析的命题,与北大师生展开了一次学术对话,下面刊发的就是这次讲座的部分内容。——编者     

Wavelet‐based estimators for mixture regression

We consider a process that is observed as a mixture of two random distributions, where the mixing probability is an unknown function of time. The setup is built upon a wavelet‐based mixture regression. Two linear wavelet estimators are proposed. Furthermore, we consider three regularizing procedures for each of the two wavelet methods. We also discuss regularity conditions under which the consistency of the wavelet methods is attained and derive rates of convergence for the proposed estimators. A Monte Carlo simulation study is conducted to illustrate the performance of the estimators. Various scenarios for the mixing probability function are used in the simulations, in addition to a range of sample sizes and resolution levels. We apply the proposed methods to a data set consisting of array Comparative Genomic Hybridization from glioblastoma cancer studies.     

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next‐Generation Sequencing Data

Next‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.     

Risk aversion in expected intertemporal discounted utilities bandit problems

We consider a situation where an individual is facing an uncertain situation, but may costly alter his knowledge of the uncertainties. We study in this context how risk aversion may modify the individual search behavior. We consider a one-armed bandit problem (where one arm is safe and the other is risky) and study how the agent risk aversion can change the sequence of arms selected. The main result is that when the utility function is more concave, the agent has more chances to select the safe arm. We also discuss how search is affected by risk aversion.     

Modelling and controlling product manufacturing systems using bond-graphs and state equations: Continuous systems and discrete systems which can be represented by continuous models

This paper presents an approach to the modelling and control phases involved in a cooperative process of design and management of the manufacturing systems. Modelling the evolution of the production flow, in the different sections of the system, is based upon the use of the bondgraph methodology in order to reach the state formalism which constitutes one of the modern representations of automation. The class of the systems studied ranges from continuous systems to discrete systems, which can be represented by a continuous approach according to the level of approximation required. The first part of this paper is dedicated to the development of generic models associated with the various basic entities of the manufacturing systems. Then, the bondgraph model of any system is obtained by assembling generic models in relation to the implementation of the means of the studied system, thus guaranteeing a representation which is quite close to the engineering sketches. Finally, switching to the state equation is performed systematically with the easiness provided by this formalism while taking into account the causality principle. An application, involving most of the elementary models developed, concludes the paper.